Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks

• URL: http://arxiv.org/abs/2409.19414v1
• Date: Sat, 28 Sep 2024 17:13:59 GMT
• Title: Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
• Authors: Mitchell Keren Taraday, Almog David, Chaim Baskin,
• Abstract summary: We introduce Sequential Signal Mixing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA treats the neighbor features as 2D discrete signals and sequentially convolves them, inherently enhancing the ability to mix features attributed to distinct neighbors. We show that when combining SSMA with well-established MPGNN architectures, we achieve substantial performance gains across various benchmarks.
• Score: 2.7719338074999547
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Message Passing Graph Neural Networks (MPGNNs) have emerged as the preferred method for modeling complex interactions across diverse graph entities. While the theory of such models is well understood, their aggregation module has not received sufficient attention. Sum-based aggregators have solid theoretical foundations regarding their separation capabilities. However, practitioners often prefer using more complex aggregations and mixtures of diverse aggregations. In this work, we unveil a possible explanation for this gap. We claim that sum-based aggregators fail to "mix" features belonging to distinct neighbors, preventing them from succeeding at downstream tasks. To this end, we introduce Sequential Signal Mixing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA treats the neighbor features as 2D discrete signals and sequentially convolves them, inherently enhancing the ability to mix features attributed to distinct neighbors. By performing extensive experiments, we show that when combining SSMA with well-established MPGNN architectures, we achieve substantial performance gains across various benchmarks, achieving new state-of-the-art results in many settings. We published our code at \url{https://almogdavid.github.io/SSMA/}

Related papers

• Alleviating Over-Smoothing via Aggregation over Compact Manifolds [19.559230417122826]
  Graph neural networks (GNNs) have achieved significant success in various applications. Most GNNs learn the node features with information aggregation of its neighbors and feature transformation in each layer. However, the node features become indistinguishable after many layers, leading to performance deterioration.
  arXiv  Detail & Related papers  (2024-07-27T11:02:12Z)
• Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering [4.264842058017711]
  Community detection is a critical challenge in analysing real graphs. This article proposes three new, general, hierarchical frameworks to deal with this task. We compare over a hundred module combinations on the Block Model graphs and real-life datasets.
  arXiv  Detail & Related papers  (2023-03-21T22:12:53Z)
• Learning with MISELBO: The Mixture Cookbook [62.75516608080322]
  We present the first ever mixture of variational approximations for a normalizing flow-based hierarchical variational autoencoder (VAE) with VampPrior and a PixelCNN decoder network. We explain this cooperative behavior by drawing a novel connection between VI and adaptive importance sampling. We obtain state-of-the-art results among VAE architectures in terms of negative log-likelihood on the MNIST and FashionMNIST datasets.
  arXiv  Detail & Related papers  (2022-09-30T15:01:35Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution [43.41163711340362]
  We design a novel GNN model, namely Mix-Moment Graph Neural Network (MM-GNN), which includes a Multi-order Moment Embedding (MME) module and an Element-wise Attention-based Moment Adaptor module. MM-GNN first calculates the multi-order moments of the neighbors for each node as signatures, and then use an Element-wise Attention-based Moment Adaptor to assign larger weights to important moments for each node and update node representations.
  arXiv  Detail & Related papers  (2022-08-15T05:59:08Z)
• Multi-scale Feature Aggregation for Crowd Counting [84.45773306711747]
  We propose a multi-scale feature aggregation network (MSFANet) MSFANet consists of two feature aggregation modules: the short aggregation (ShortAgg) and the skip aggregation (SkipAgg)
  arXiv  Detail & Related papers  (2022-08-10T10:23:12Z)
• Graph Ordering Attention Networks [22.468776559433614]
  Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data. We introduce the Graph Ordering Attention (GOAT) layer, a novel GNN component that captures interactions between nodes in a neighborhood. GOAT layer demonstrates its increased performance in modeling graph metrics that capture complex information.
  arXiv  Detail & Related papers  (2022-04-11T18:13:19Z)
• Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks [127.32203532517953]
  We develop a vanilla 1-bit framework that binarizes both the GNN parameters and the graph features. Despite the lightweight architecture, we observed that this vanilla framework suffered from insufficient discriminative power in distinguishing graph topologies. This discovery motivates us to devise meta aggregators to improve the expressive power of vanilla binarized GNNs.
  arXiv  Detail & Related papers  (2021-09-27T08:50:37Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)
• Graph Neural Networks with Composite Kernels [60.81504431653264]
  We re-interpret node aggregation from the perspective of kernel weighting. We present a framework to consider feature similarity in an aggregation scheme. We propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.
  arXiv  Detail & Related papers  (2020-05-16T04:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.